High-Efficiency Compact Heat Exchangers and Condensers for Small Modular Energy Systems
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20 FOSSIL-FUELED POWER PLANTS↗
SEARCH · Engineering Papers
Results for “energy efficient materials”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Thermal Dispersion Within a Porous Medium Near a Solid Wall
The regenerator is a key component to Stirling cycle machine efficiency. Typical regenerators are of sintered fine wires or layers of fine-wire screens. Such porous materials are contained within solid-waH casings. Thermal energy exchange between the regenerator and the casing is important to cycle performance for the matrix and casing would not have the same axial temperature profile in an actual machine. Exchange from one to the other may allow shunting of thermal energy, reducing cycle efficiency. In this paper, temperature profiles within the near-wall region of the matrix are measured and thermal energy transport, termed thermal dispersion, is inferred. The data show how the wall affects thermal transport. Transport normal to the mean flow direction is by conduction within the solid and fluid and by advective transport within the matrix. In the near-wall region, both may be interrupted from their normal in-core pattern. Solid conduction paths are broken and scales of advective transport are damped. An equation is presented which describes this change for a wire screen mesh. The near-wall layer typically acts as an insulating layer. This should be considered in design or analysis. Effective thermal conductivity within the core is uniform. In-core transverse thermal effective conductivity values are compared to direct and indirect measurements reported elsewhere and to 3D numerical simulation results, computed previously and reported elsewhere. The 3-D CFD model is composed of six cylinders in cross flow, staggered in arrangement to match the dimensions and porosity of the matrix used in the experiments. The commercial code FLUENT is used to obtain the flow and thermal fields. The thermal dispersion and effective thermal conductivities for the matrix are computed from the results.
Simon, T.↗
Survey study of the efficiency and economics of hydrogen liquefaction
The production of liquid hydrogen, with coal as the starting material, is reported. The minimum practicable energy and cost for liquefaction of gaseous hydrogen in the 1985-2000 time period is presented to investigate the possible benefits of the integration of coal gasification processes with the liquefaction process.
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Synthesis and Direct Manufacturing of High Conductivity Aluminum Alloy Conductors Using an Energy Efficient Solid-State Process and Their Advanced Characterization
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36 MATERIALS SCIENCE↗
Solvent-Free Self-Assembly for Scalable Preparation of Highly Crystalline Mesoporous Metal Oxides
Mesoporous metal oxides (MMOs) have been demonstrated great potential in various applications. Up to now, the direct synthesis of MMOs is still limited to the solvent induced inorganic-organic self-assembly process. In this work, we develop a facile, general, and high throughput solvent-free self-assembly strategy to synthesize a series of MMOs including single-component MMOs and multi-component MMOs (e.g., doped MMOs, composite MMOs, and polymetallic oxide) with high crystallinity and remarkable porous properties by grinding and heating raw materials. Compared with the traditional solution self-assembly process, the avoidance of solvents in this method not only greatly increases the yield of target products and synthesis efficiency, but also reduces the environmental pollution and the consumption of cost and energy. We believe the presented approach will pave a new avenue for scalable production of advanced mesoporous materials for various applications.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Plasmon Energy Transfer in Hybrid Nanoantennas
Plasmonic metal nanoparticles exhibit large dipole moments upon photoexcitation and have the potential to induce electronic transitions in nearby materials, but fast internal relaxation has to date limited the spatial range and efficiency of plasmonic mediated processes. In this work, we use photo-electrochemistry to synthesize hybrid nanoantennas comprised of plasmonic nanoparticles with photoconductive polymer coatings. Here, we demonstrate that the formation of the conductive polymer is selective to the nanoparticles and that polymerization is enhanced by photoexcitation. In situ spectroscopy and simulations support a mechanism in which up to 50% efficiency of nonradiative energy transfer is achieved. These hybrid nanoantennas combine the unmatched light-harvesting properties of a plasmonic antenna with the similarly unmatched device processability of a polymer shell.
36 MATERIALS SCIENCE↗
High-Performance Thermoelectric Generators for Field Deployments
Thermoelectric power generation is a reliable energy harvesting technique for directly converting heat into electricity. Recent studies have reported the thermal-to-electrical energy conversion efficiency of thermoelectric generators (TEGs) up to 11% under laboratory settings. However, the practical effectiveness of TEGs deployed under real environments is still not more than a few percent. In this study, we provide fundamental insight on the operation of TEGs in realistic environments by illustrating the combinatory effect of thermoelectric material properties, device boundary conditions, and environmental thermal resistivity on TEG performance in conjunction with the module parameters. Using numerical and experimental studies, we demonstrate the existence of a critical heat transfer coefficient that dramatically affects the design and performance of TEGs. Results provide a set of concrete design criteria for developing efficient TEGs that meet the metrics for field deployments. High-performance TEGs demonstrated in this study generated up to 28% higher power and 162% higher power per unit mass of thermoelectric materials as compared to the commercial module deployed for low-grade waste heat recovery. This advancement in understanding the TEG operation will have a transformative impact on the development of scalable thermal energy harvesters and in realizing their practical targets for efficiency, power density, and total output power.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
First-Principles Evaluation of Proton Hopping in Tetrahedral Oxide Motifs
Proton-conducting oxides (PCOs) are important materials used as ionic conductors for energy conversion technologies. Existing research efforts on PCO optimization and discovery generally focus on complex perovskite-based oxides that require doping and alloying to engineer oxygen deficiency and high proton conductivity. However, the variety of chemical compositions and coordination environments in oxides poses challenges for efficient materials design. In this computational study, we construct a database of simplified motifs to elucidate the relationship between fundamental materials chemistry and proton kinetics. Specifically, we focus on the zincblende crystal structure as a proxy for tetrahedral metal–oxide (M–O) coordination environments. We systematically quantified the effects of cation type, oxidation states, and M–O bond lengths on the proton hopping barrier, and found that strong M–O bonds and metal cations with large and variable oxidation states (e.g., Mo 6+ , V 5+ ) lead to smaller proton hopping barriers. By mapping the candidate cations and their preferred bond geometries onto materials databases such as the Inorganic Crystal Structure Database (ICSD) and Materials Project, we identified real materials containing the corresponding metal–oxide units. In general, we observed good agreement between the calculated proton hopping barriers obtained in real crystal structures and those predicted by our motif database. We also discuss the limitations of our model and possible future extensions to improve its predictive capabilities. Overall, our model provides a first step for the rational design and quick screening of energy-efficient PCOs.
organic↗
Discrimination of xylene isomers in a stacked coordination polymer
The separation and purification of xylene isomers is an industrially important but challenging process. Developing highly efficient adsorbents is crucial for the implementation of simulated moving bed technology for industrial separation of these isomers. Herein, we report a stacked one-dimensional coordination polymer {[Mn(dhbq)(H 2 O) 2 ], H 2 dhbq = 2,5-dihydroxy-1,4-benzoquinone} that exhibits an ideal molecular recognition and sieving of xylene isomers. Its distinct temperature-adsorbate–dependent adsorption behavior enables full separation of p -, m -, and o -xylene isomers in both vapor and liquid phases. The delicate stimuli-responsive swelling of the structure imparts this porous material with exceptionally high flexibility and stability, well-balanced adsorption capacity, high selectivity, and fast kinetics at conditions mimicking industrial settings. This study may offer an alternative approach for energy-efficient and adsorption-based industrial xylene separation and purification processes.
Science & Technology - Other Topics↗
Process scale-up and optimization of the metal-organic framework synthesis
Mosaic Materials is incubated under Cyclotron Road LBNL, focusing on design, synthesis, and characterization of metal-organic frameworks as highly selective and energy efficient adsorbents for carbon dioxide. The Chief Science Officer, Dr. Thomas McDonald, invented a new class of phase-change adsorbents for acid gas removal that the company is working to commercialize as its first product. ABPD worked with Mosaic team to scale up their synthesis process and evaluate the downstream processing.
36 MATERIALS SCIENCE↗
In-Situ TEM Molten Salt Corrosion
Molten salt reactors (MSRs) offer a compelling pathway for next-generation nuclear energy, with advantages in thermal efficiency, inherent safety, and flexible fuel management. Yet, halide-based molten salts introduce significant materials challenges, particularly alloy corrosion. Alloy performance in these environments ultimately depends on understanding how corrosion initiates and progresses at the nanoscale, however most existing models rely on post-exposure characterization, leaving degradation mechanisms largely inferred rather than directly observed. NiCr alloys have garnered interest in MSRs applications as the Ni-based matrix provides strength and creep resistance, while Cr content offers oxidation resistance in air. However, NiCr corrosion resistance in chloride salts has proven poor due to preferential chromium dissolution, the formation of Cr-depleted pathways, and grain-boundary attack. This work aims to directly visualize corrosion of Ni-20Cr exposed to LiCl-KCl using in-situ Transmission Electron Microscopy (TEM) to capture real-time microstructural evolution during corrosion. Experiments will be performed at ~800 °C under controlled pressure conditions while utilizing Energy-Dispersive X-ray Spectroscopy (EDS) to analyze elemental redistribution. Observation of chromium depletion fronts, associated surface restructuring, and localized chloride enrichment are expected. Ultimately, this study is expected to provide a link between microscale processes and the macroscopic degradation behaviors relevant to MSR operation in advanced reactor environments. Simultaneously, this approach enables future in-situ investigations regarding alloy composition, salt chemistry, and their influence on corrosion pathways and long-term stability.?
36 - MATERIALS SCIENCE↗
Saddle point search with dynamic active volume
Sampling potential energy surface (PES) is critical for many problems in materials science, chemistry, physics, and biology and requires highly efficient saddle point searches (SPS). In the study, we introduce the concept of dynamic active volume (DAV) in addition to the active volume in self-evolving atomistic kinetic Monte Carlo (SEAKMC). The DAV method has further reduced the dimensionality of the PES at the elevation stage of a SPS. At the subsequent converging stage, the dynamic boundary in DAV is lifted to allow the system to converge to the right location of a saddle point. Coupled with the dimer method, the DAV method not only significantly reduces the time cost for a given search attempt, but also dramatically increases the probability of finding relevant saddle points for PES sampling. Further, a Python software package within the framework SEAKMC (SEAKMC_py) with the DAV method has been developed.
36 MATERIALS SCIENCE↗
Impact of electric vehicle battery recycling on reducing raw material demand and battery life-cycle carbon emissions in China
The rapid growth of electric vehicles (EVs) in China challenges raw material demand. This study evaluates the impact of recycling and reusing EV batteries on reducing material demand and carbon emissions. Integrating a national-level vehicle stock turnover model with life-cycle carbon emission assessment, we found that replacing nickel-cobalt-manganese batteries with lithium iron phosphate batteries with battery recycling can reduce lithium, cobalt, and nickel demand between 2021 and 2060 by up to 7.8 million tons (Mt) (67%), 12.4 Mt (96%), and 37.2 Mt (93%), respectively, significantly decreasing reliance on import. Moreover, battery recycling coupled with reuse can reduce carbon emissions by up to 6,532-6,864 Mt (36.0-37.9%), depending on four recycling methods employed. However, this reuse strategy delays battery recycling and risks lithium supply shortage, necessitating trade-offs between carbon reduction and material supply. Future technologies, such as lithium-sulfur and all-solid-state batteries, despite their energy efficiency, might exacerbate lithium shortage, underscoring the crucial need for increased lithium supply.
25 ENERGY STORAGE↗
PAL 2.0: a physics-driven bayesian optimization framework for material discovery
PAL 2.0 provides an efficient discovery tool for advanced functional materials, ameliorating a major bottleneck to enabling advances in next-generation energy, health, and sustainability technologies.
Chemistry↗
Accelerating catalytic advancements through the precision of high-throughput experiments & calculations
The growing demand for energy-efficient processes to support a sustainable future drives the need for research to rapidly explore chemical and material space through accelerated catalyst discovery initiatives. Recent breakthroughs in high-throughput experimental and computational methods are transforming the catalysis field, surpassing traditional approaches to manipulating variables in catalytic processes. Key advancements in innovation include the integration of machine learning for efficient catalyst screening, high-throughput experimentation, data-driven methodologies employing comprehensive databases, and in situ and in operando techniques for realistic observations. This progress has undoubtedly been intertwined with a collaborative framework across disciplines, reshaping catalyst discovery methods in both industry and academia. This Opinion article presents a multifaceted perspective from coauthors with expertise spanning various stages of the Technology Readiness Level spectrum, highlighting both opportunities and persistent challenges in integrating computational and experimental approaches in catalysis. These challenges span from obtaining high-quality experimental data, scaling simulations to industrially relevant materials and process conditions to navigating the complexity and predictive accuracy of computational models.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
An efficient protocol for excited states of large biochromophores
Efficient energy transport in photosynthetic antenna is a long-standing source of inspiration for artificial light harvesting materials. However, characterizing the excited states of the constituent chromophores poses a considerable challenge to mainstream quantum chemical and semiempirical excited state methods due to their size and complexity and the accuracy required to describe small but functionally important changes in their properties. In this paper, we explore an alternative approach to calculating the excited states of large biochromophores, exemplified by a specific method for calculating the Q y transition of bacteriochlorophyll a, which we name Chl-xTB. Using a diagonally dominant approximation to the Casida equation and a bespoke parameterization scheme, Chl-xTB can match time-dependent density functional theory’s accuracy and semiempirical speed for calculating the potential energy surfaces and absorption spectra of chlorophylls. We demonstrate that Chl-xTB (and other prospective realizations of our protocol) can be integrated into multiscale models, including concurrent excitonic and point-charge embedding frameworks, enabling the analysis of biochromophore networks in a native environment. We exploit this capability to probe the low-frequency spectral densities of excitonic energies and interchromophore interactions in the light harvesting antenna protein LH2 (light harvesting complex 2). The impact of low-frequency protein motion on interchromophore coupling and exciton transport has routinely been ignored due to the prohibitive costs of including it in simulations. Our results provide a more rigorous basis for continued use of this approximation by demonstrating that exciton transition energies are unaffected by low-frequency vibrational coupling to exciton interaction energies.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Design of a Nanophotonic Hybrid Coupler
This project aims to design and simulate a nanophotonic hybrid coupler for detecting vacuum ultraviolet (VUV) light with higher efficiency. VUV light is of significant interest because of its applications ranging from high energy physics to space science and electronic industry. However, current VUV photodetectors have very low efficiencies because most materials absorb in the VUV region (100-200 nm). To address this challenge, I have designed a plasmonic-dielectric coupler with wavelength shifting properties, enabling high Purcell enhancement and far-field confinement. The coupler consists of a cylindrical cSi resonator on top of a thin ZnO layer, and Ag and SiO2 are used as substrates. Simulations are done using the finite-difference time domain method to solve Maxwell’s equations, and specifically with Meep and Lumerical. The effectiveness of the coupler is characterized by the Purcell factor and far-field radiation. The Purcell factor reaches a maximum near 390 nm and is in the o rder of 10^4. Furthermore, the coupler also allows partial control of the directionality of the far-field radiation. With the presence of the resonator, the radiation is directed towards the coupled metalens. Overall, our design combines the advantages of plasmonic and dielectric couplers, along with the intrinsic wavelength shifting properties of materials to enhance VUV detection significantly, and such a device can be used in, for example, the DUNE project.
Zhou, Jiayang↗
Application of Machine Learning and Data Augmentation Algorithms in the Discovery of Metal Hydrides for Hydrogen Storage
The development of efficient and sustainable hydrogen storage materials is a key challenge for realizing hydrogen as a clean and flexible energy carrier. Among various options, metal hydrides offer high volumetric storage density and operational safety, yet their application is limited by thermodynamic, kinetic, and compositional constraints. In this work, we investigate the potential of machine learning (ML) to predict key thermodynamic properties—equilibrium plateau pressure, enthalpy, and entropy of hydride formation—based solely on alloy composition using Magpie-generated descriptors. We significantly expand an existing experimental dataset from ~400 to 806 entries and assess the impact of dataset size and data augmentation, using the PADRE algorithm, on model performance. Models including Support Vector Machines and Gradient Boosted Random Forests were trained and optimized via grid search and cross-validation. Results show a marked improvement in predictive accuracy with increased dataset size, while data augmentation benefits are limited to smaller datasets and do not improve accuracy in underrepresented pressure regimes. Furthermore, clustering and cross-validation analyses highlight the limited generalizability of models across different material classes, though high accuracy is achieved when training and testing within a single hydride family (e.g., AB2). The study demonstrates the viability and limitations of ML for accelerating hydride discovery, emphasizing the importance of dataset diversity and representation for robust property prediction.
augmentation↗